Author
Listed:
- Yanbing Chen
(School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)
- Ke Wang
(College of Information Engineering, Jiangmen Polytechnic, Jiangmen 529000, China)
- Hairong Ye
(School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)
- Lingbing Tao
(School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)
- Zhixin Tie
(School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
KeYi College, Zhejiang Sci-Tech University, Shaoxing 312369, China)
Abstract
Person re-identification (ReID) refers to the task of retrieving target persons from image libraries captured by various distinct cameras. Over the years, person ReID has yielded favorable recognition outcomes under typical visible light conditions, yet there remains considerable scope for enhancement in challenging conditions. The challenges and research gaps include the following: multi-modal data fusion, semi-supervised and unsupervised learning, domain adaptation, ReID in 3D space, fast ReID, decentralized learning, and end-to-end systems. The main problems to be solved, which are the occlusion problem, viewpoint problem, illumination problem, background problem, resolution problem, openness problem, etc., remain challenges. For the first time, this paper uses person ReID in special scenarios as a basis for classification to categorize and analyze the related research in recent years. Starting from the perspectives of person ReID methods and research directions, we explore the current research status in special scenarios. In addition, this work conducts a detailed experimental comparison of person ReID methods employing deep learning, encompassing both system development and comparative methodologies. In addition, we offer a prospective analysis of forthcoming research approaches in person ReID and address unresolved concerns within the field.
Suggested Citation
Yanbing Chen & Ke Wang & Hairong Ye & Lingbing Tao & Zhixin Tie, 2024.
"Person Re-Identification in Special Scenes Based on Deep Learning: A Comprehensive Survey,"
Mathematics, MDPI, vol. 12(16), pages 1-19, August.
Handle:
RePEc:gam:jmathe:v:12:y:2024:i:16:p:2495-:d:1455235
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